tune.doc: Shared documentation of the arguments every learning method...

In fdm2id: Data Mining and R Programming for Beginners

Shared documentation of the arguments every learning method takes

Description

This function is never called: it holds the canonical documentation of the four arguments that close the signature of every classification and regression method of the package, shared through @inheritParams rather than repeated in some thirty places. A method whose own @param says something more specific keeps it.

Usage

tune.doc(tune, methodparameters, graph, seed, nfolds)

Arguments

tune	If true, the function returns parameters instead of a classification model.
methodparameters	Pre-tuned parameters, as returned by the same method called with tune = TRUE. performance obtains them once and passes them back when fitting, so that the tuning is not redone on every split. A method with nothing to tune returns an empty object, which leaves its defaults untouched.
graph	Whether the method draws the graphic that goes with its tuning (the cross-validation curve, typically). Methods that have no such graphic accept the argument and ignore it.
seed	A specified seed for random number generation, so that two runs on the same data give the same model. Every learning method accepts it, so that it can be set the same way whatever the method; the deterministic ones simply have nothing to draw and give the same model with or without it.
nfolds	The number of folds of the cross-validation a method runs to choose its hyperparameters. Only used when there is something to choose, i.e. when one of them is given as a vector. Lower it to fit faster, at the cost of a noisier choice.

Details

Every learning method of the package ends on the same four arguments, in the same order: tune, methodparameters, graph, seed. That is what lets performance take any of them without knowing which, and what lets one method be replaced by another in a script without rewriting the call.

fdm2id documentation built on Aug. 28, 2026, 9:07 a.m.